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Article type: Research Article
Authors: Wu, Yanqiu; * | Liu, Min | Sun, Dehong
Affiliations: Minnan University of Science and Technology, Quanzhou, Fujian, China
Correspondence: [*] Corresponding author. Yanqiu Wu, Minnan University of Science and Technology, Quanzhou, Fujian, China. Email: 1369343049@qq.com.
Abstract: Person re-identification relies on discriminative features. However, most researches focus on extracting features from the high-layer of network while ignoring the middle-layer features, some important details are overlooked frequently. To address this issue, we propose a Multi-Scale and Multi-Patch Feature Fusion Network(MSPF). We employ modified OSFA to extract, align, and fuse the feature maps in the middle-layer of network, which can compensate for the lack of detailed information in the high-level network features. To obtain richer detailed global features of pedestrian, we construct a multi-patch feature fusion module(MPF). We concatenate the global features extracted from modified OSFA and MPF to obtain global features with richer detailed representations. Cross-entropy loss, triplet loss and center loss are combined to constrain our model. We evaluate the performance of our model on Market-1501, CUHK03_labeled and DukeMTMC. The results prove that our method is superior to the state-of-the-art approaches.
Keywords: Person re-identification, multi-scale, multi-patch, feature fusion
DOI: 10.3233/JIFS-237113
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7603-7612, 2024
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